ObjectiveTo evaluate the public health governance capacity in Jiangsu Province and provide an optimized pathway for the construction of a “strong, rich, beautiful, and high-quality” new Jiangsu. MethodsA total of 806 policy documents, 658 public information reports, and 148 research literatures related to public health governance capacity in Jiangsu Province from January 1995 to December 2023 were collected. The status of current public health goverance was assessed based on the evaluation criteria suitable for public health systems, and the strengths and the weaknesses of the system were identified. ResultsThe public health governance capability of Jiangsu Province was scored at 738.3 points, ranking 3rd nationally. Maternal health care and emergency response capacities achieved leading positions nationwide, both ranking 2nd. Jiangsu had exhibited a standardized guidance in the strategic level, a well-established management mechanism, an extensive coverage in information collection, and a scientifically established health targets setting. However, bottlenecks remained, including an unclear division of responsibilities across organizational departments, an insufficient public-health workforce, the absence of a stable growth mechanism for government funding investment, and difficulties in promptly identifying public needs. ConclusionJiangsu’s public-health system demonstrates leading nationally, yet several components remain underdeveloped. Future efforts should consolidate advantages while addressing weaknesses, further diversify content and forms, establish a stable funding increase mechanism, and clarify departmental functions, thereby providing solid health support for realizing the developmental goals of a “strong, rich, beautiful and high-quality” new Jiangsu.

ObjectiveTo systematically assess the public health governance capacity in Zhejiang Province, to conduct an in-depth analysis of its strengths and weaknesses, so as to provide scientific basis and strategic recommendations for further enhancement. MethodsA systematic collection of policy documents, public information reports, and research literature related to public health governance capacity in Zhejiang Province from 2002 to 2023 was conducted (encompassing a total of 1 263 policy documents, 138 pieces of information reports and 631 research articles). Based on the evaluation criteria suitable for public health systems previously developed by the research team, the basic status and magnitude of change in public health governance capacity in Zhejiang Province was evaluated. Additionally, normative gap analyses were employed to identify the strengths and weaknesses. ResultsZhejiang Province ranked 4th nationwide in terms of public health governance capacity with a score of 733.4 points (1 000.0-point maximum). The province has effectively implemented the principle of health first (scoring 698.5 points in the assessment of health-first strategy implementation) and attached sufficient importance to health-related goals (scoring 658.2 points in the scientific rationality of goal setting). However, the implementation of inter-departmental coordination and incentive mechanisms only scored 178.7 points, the feasibility of management and monitoring mechanisms scored even lower at only 144.0 points, and the coverage of incentive mechanisms scored 286.0 points. ConclusionZhejiang Province has effectively implemented its health first strategy and attached great importance to health targets, but still needs to strengthen cross-departmental coordination mechanisms and health-oriented incentives.

ObjectiveThis study aims to explore the mechanism by which Yishen Huoxue Tongqiao Formula improves metabolism-neuroplasticity and treats unilateral vestibular labyrinth destruction by regulating the metabolic balance of glutamate （Glu）/γ-aminobutyric acid （GABA）. Methods48 Sprague-Dawley （SD） adult rats were randomly divided into the sham operation group， model group， Yishen Huoxue Tongqiao Formula groups with low， medium， and high doses （9.20， 18.39， 36.78 g·kg^-1）， and betahistine group （1.62 mg·kg^-1）. A unilateral vestibular labyrinth destruction （vestibular dysfunction） model was established by intratympanic injection of chloroform into the right ear， while the control group received intratympanic injection of normal saline. Drugs were administered once daily for seven consecutive days. During the period， behavioral tests were performed to evaluate the behaviors of rats after unilateral vestibular labyrinth destruction. Hematoxylin-eosin （HE） staining and Nissl staining were used to observe the neuronal morphology in the medial vestibular nucleus. Golgi staining was employed to assess the number of dendritic spines of neurons in the medial vestibular nucleus. Ultra-performance liquid chromatography-tandem mass spectrometry （LC-ESI-MS/MS） was utilized to detect Glu/GABA. Immunofluorescence and immunohistochemistry were used to detect the expressions of neuronal nuclei （NeuN）， growth-associated protein 43 （GAP-43）， and glial fibrillary acidic protein （GFAP）. Western blot and real-time fluorescent quantitative polymerase chain reaction （Real-time PCR） were applied to determine the expressions of glutamate-immunoreactive （Glu-IR）， GABA， GFAP， postsynaptic density protein 95 （PSD-95）， and GAP-43. ResultsCompared with the sham operation group， the model group presented with head deviation， balance disorder， increased tail suspension score， nuclear consolidation of medial vestibular nerve neurons， and decreased Nissl bodies （P<0.01）. The number of dendritic spines in neurons and NeuN-positive cells decreased. The content of Glu decreased. The content of GABA increased （Glu/GABA decreased）. The expression of GAP-43 was down-regulated， and GFAP was up-regulated （P<0.05， P<0.01）. The expressions of Glu-IR， PSD-95， and GAP-43 proteins， as well as Glu-IR mRNA decreased， while the expressions of GABA and GFAP proteins and mRNA increased （P<0.05， P<0.01）. Compared with those in the model group， the head deviation， imbalanced behavior， and tail suspension scores in each treatment group decreased， with alleviated neuronal injury and recovered Nissl bodies （P<0.01）. The number of dendritic spines of neurons increased， and the number of NeuN-positive cells rebounded. The content of Glu increased， and the content of GABA decreased （Glu/GABA increased）. GFAP was down-regulated， and GAP-43 was up-regulated （P<0.05， P<0.01）. The expressions of Glu-IR， PMD-95， and GAP-43 proteins， as well as Glu-IR mRNA increased， while the expressions of GABA and GFAP proteins and mRNA decreased. The effect was more significant in the high-dose group （P<0.01）. ConclusionThe Yishen Huoxue Tongqiao Formula can alleviate vestibular dysfunction， and its mechanism may be associated with regulating the metabolic balance of Glu/GABA， mitigating neural damage， improving synaptic plasticity （promoting GAP-43 expression and inhibiting GFAP expression）， and facilitating vestibular compensation.

BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

Objective Objective To analyze the potential spatial factors affecting the genetic differentiation of Oncomelania hupensis robertsoni in Yunnan Province. Methods A total of 13 administrative villages were selected from schistosomiasis-endemic areas of Yunnan Province as O. hupensis snail sampling sites. At least 200 snails were collected in each site, and the spatial variable data of each site were recorded, including longitude, latitude and altitude. Thirty active and Schistosoma japonicum uninfected O. hupensis snails were selected from each sampling site by means of the crawling method and the cercarial shedding method. Genomic DNA was extracted from O. hupensis snails. Following PCR amplification, purification of PCR amplification products and sequencing, the gene sequences of O. hupensis snail samples were spliced and edited using the DNAstar software and the NCBI database to yield the complete mitochondrial sequences of O. hupensis snails at each sampling site, and the mitochondrial genetic distance matrix of O. hupensis robertsoni was calculated at each sampling site. The geographical coordinates of each sampling site were marked using the software ArcGIS 10.2, and the straight-line geographical distance between each sampling site was calculated. The altitude difference, longitude difference and latitude difference between each sampling site were calculated using the Excel software, and the correlation between the mitochondrial genetic distance matrix of O. hupensis robertsoni and each spatial variable matrix was examined by using the Mantel test at 13 sampling sites in Yunnan Province. Results Among the 13 O. hupensis snail sampling sites in Yunnan Province, the largest mitochondrial genetic distance of O. hupensis robertsoni snail populations was seen between Anding Village, Nanjian Yi Autonomous County and Caizhuang Village, Midu County (26.244 2), and the largest geographical distance was seen between Dongyuan Village, Gucheng District and Cangling Village, Chuxiong County (272.64 km). The highest altitude difference was seen between Anding Village, Nanjian Yi Autonomous County and Dongyuan Village, Gucheng District (1 086.10 m), and the largest longitude difference was found between Qiandian Village, Eryuan County and Cangling Village, Chuxiong County (1.86°), while the largest latitude difference was measured between Leqiu Village, Nanjian Yi Autonomous County and Dongyuan Village, Gucheng District (1.81°). In addition, the mitochondrial genetic distance of O. hupensis robertsoni snail populations was positively correlated with altitude at 13 snail sampling sites in Yunnan Province (r = 0.542 8, P < 0.001), and showed no significant correlations with geographical distance (r = 0.093 4, P > 0.05), longitude (r = −0.199 5, P > 0.05) or latitude (r = 0.205 7, P > 0.05). Conclusion Altitude may be a potential spatial factor affecting the genetic differentiation of O. hupensis robertsoni in Yunnan Province.

Background Co-exposure to noise and microwave radiation occurs frequently. The central nervous system has been identified as a sensitive target organ for both noise and microwave exposure individually, and the underlying mechanisms remain poorly understood. The specific biological effects resulting from co-exposure to these two factors have yet to be fully elucidated. Objective To clarify the effects of co-exposure to noise and microwave on neurobehavior and hippocampal tissue structure, and to explore the underlying mechanism through the assessment of serum cytokines. Methods C57BL/6N mice were selected and randomly assigned to a blank control group, a noise group, a microwave group, and a combined noise & microwave exposure group. To establish the exposure models, the noise group was subjected to broadband noise at 100 dB for 2 h, while the microwave group received radiation at a central frequency of 9.375 GHz with an average power density of 12 mW·cm⁻² and a specific absorption rate of 2.58 W·kg⁻¹ for 15 min. Open field and tail suspension tests assessed anxiety-like emotional behaviour; novel object recognition and Y-maze tests evaluated cognitive function. Histological changes in hippocampal tissue were examined using haematoxylin and eosin (HE) staining, and Nissl staining under light microscopy. Serum cytokine levels were measured using radioimmunoassay and enzyme-linked immunosorbent assay (ELISA). Results After 3 d of exposure, the noise, microwave, and combined exposure groups showed significant reductions in exploration frequency, duration, and distance within the central zone of the open field test compared to the control group (P < 0.01); the combined exposure group exhibited increased ratios of peripheral-to-central exploration time and distance (P < 0.05). After 7 d of exposure, compared with the control group, the noise group maintained a decrease in central zone exploration time (P < 0.01), while the combined exposure group showed persistent decline across all central zone metrics (P < 0.05) and elevated peripheral-to-central ratios (P < 0.05); compared to the microwave group, the combined exposure group showed significant less time in the central zone (P < 0.05) and higher peripheral-to-central ratios (P < 0.05). Regarding behaviour and cognition, compared with the control group, the combined exposure group showed increased immobility time in the tail suspension test after 3 d of exposure (P < 0.01). At this interval, all exposure groups demonstrated reduced frequency and duration of novel object recognition (P < 0.05), with the combined exposure group showing a marked decrease in novel arm exploration time (P < 0.01). After 7 d of exposure, compared with the control group, the noise group showed reduced novel object recognition frequency (P < 0.05), and both the noise and microwave groups exhibited decreased novel arm exploration time (P < 0.05). Pathological alterations including an increased number of hyperchromatic nuclei and depleted Nissl bodies were observed in the CA3 and DG regions across all exposure groups with the most severe lesions observed in the combined exposure group. Serum levels of central nervous system-specific protein β (S-100β), glial fibrillary acidic protein (GFAP), and corticosterone (CORT) were significantly elevated in all exposure groups compared with the control group (P < 0.05). Aquaporin-4 (AQP4) levels increased in the combined exposure group (P < 0.05), while CXC chemokine ligand 10 (CXCL10) levels rose in both the noise and combined groups compared with the control group (P < 0.05). Specifically, S-100β and CXCL10 levels in the combined exposure group were higher than those in the microwave group (P < 0.05); moreover, levels of S-100β, GFAP, CORT, AQP4, and CXCL10 in the combined exposure group were significantly higher than those in the noise group (P < 0.05). Conclusion Combined exposure to noise and microwave radiation induces pathological changes in the hippocampus of mice, increases levels of serum stress hormones and neuro-specific biomarkers. These impairments are more severe than those observed following single-factor exposure. The underlaying mechanism may be related to systemic stress response, neuronal damage, astrocyte activation, and changes in blood-brain barrier permeability, leading to emotional behavioral abnormalities and cognitive decline.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

OBJECTIVE To investigate the clinical characteristics of euglycemic diabetic ketoacidosis （euDKA） induced by empagliflozin， and provide references for safe medication. METHODS Case reports of euDKA induced by empagliflozin were retrieved and analyzed from PubMed， Web of Science， CNKI， and Wanfang Data. The Naranjo’s scale was adopted to assess the causal relationship between empagliflozin and euDKA. RESULTS A total of 29 patients were enrolled， including 16 males and 13 females，with a median age of 58 years. All patients were diagnosed with type 2 diabetes mellitus； 22 patients had at least one comorbidity. The median time to euDKA onset was 60 days after administration， and 7 cases occurred within 2 weeks of treatment. Perioperative periods，fasting，and low-carbohydrate or ketogenic diets were the main predisposing factors. Common presentations included nausea，vomiting， fatigue， tachypnea，and tachycardia. The causal relationship between empagliflozin and euDKA was assessed as “probable” in all cases. According to adverse reaction severity grading， 13 cases were grade 3 （severe） and 16 cases were grade 4 （life-threatening）. All patients discontinued empagliflozin and recovered after corresponding treatment. CONCLUSIONS euDKA is a serious adverse reaction to empagliflozin， which mostly occurs early in treatment. Perioperative periods， fasting， and ketogenic diets are prone to induce euDKA. Elderly patients and those with comorbidities are at higher risk. Risk assessment should be conducted before clinical administration； blood ketones and acid-base monitoring should be strengthened in the early medication period； withhold the drug before elective surgery， avoid prolonged fasting；upon diagnosis， discontinue empagliflozin and initiate intravenous insulin， provide fluid resuscitation promptly， and use other treatments to improve patient prognosis.

ObjectiveTo construct and validate a risk assessment model for post-transplant diabetes mellitus （PTDM） using multiple machine learning algorithms， and to realize the early identification of PTDM. MethodsA retrospective analysis was performed for the clinical data of the patients who underwent allogeneic liver transplantation in The First Hospital of Shanxi Medical University from April 1， 2020 to December 31， 2024， and they were randomly divided into a training set and a validation set at a ratio of 7∶3. The LASSO regression analysis combined with 5-fold cross-validation was used for feature selection. Seven machine learning models were developed in the training set， i.e.， logistic regression （LR）， decision tree （DT）， Naive Bayes （NB）， random forest （RF）， K-nearest neighbor （KNN）， extreme gradient boosting （XGBoost）， and adaptive boosting （AdaBoost）. In the validation set， various methods were used to assess the predictive performance of each model， such as accuracy， precision， recall rate， specificity， F1 score， area under the receiver operating characteristic curve （AUROC）， and area under the precision-recall curve （AUPRC）. The Brier score and decision curve analysis were used to assess the calibration and clinical practicability of the models， and the SHAP method was used to analyze feature importance. The independent-samples t test or the Wilcoxon rank-sum test was used for comparison of continuous data between two groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. ResultsA total of 135 liver transplant recipients were enrolled， among whom 26 developed PTDM， and there were 94 patients in the training set and 41 in the validation set. Feature extraction and screening identified 7 key features of sex， overweight or obesity， anhepatic phase， time of operation， length of hospital stay， early postoperative hypomagnesemia， and impaired fasting glucose （IFG）. In the validation set， the XGBoost model showed the best predictive performance， with an AUROC of 0.907 （95% confidence interval ［CI］： 0.807 — 0.989）， an AUPRC of 0.649 （95%CI： 0.339 — 0.955）， an accuracy of 0.878， a precision of 0.667， a recall rate of 0.750， an F1-score of 0.706， a specificity of 0.909， and a Brier score of 0.104. The decision curve analysis showed that when the threshold probability was below 0.667， application of the XGBoost model in clinical decision-making provided relatively high net benefit. The SHAP analysis showed that the length of hospital stay ranked first in terms of feature importance， followed by time of operation， overweight or obesity， sex， IFG， anhepatic phase， and early postoperative hypomagnesemia. ConclusionThe risk assessment model for PTDM in liver transplant recipients based on XGBoost algorithm has excellent performance and can effectively identify high-risk individuals； however， multicenter large-sample data are needed for further validation.

BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.